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HilkerThomasForestrySunlightMediatedSeasonalitySupplementaryInfo.pdf

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https://ir.library.oregonstate.edu/concern/articles/tx31qk59z

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  • Resolving the debate surrounding the nature and controls of seasonal variation in the structure and metabolism of Amazonian rainforests is critical to understanding their response to climate change. In situ studies have observed higher photosynthetic and evapotranspiration rates, increased litterfall and leaf flushing during the Sunlight-rich dry season. Satellite data also indicated higher greenness level, a proven surrogate of photosynthetic carbon fixation, and leaf area during the dry season relative to the wet season. Some recent reports suggest that rainforests display no seasonal variations and the previous results were satellite measurement artefacts. Therefore, here we re-examine several years of data from three sensors on two satellites under a range of sun positions and satellite measurement geometries and document robust evidence for a seasonal cycle in structure and greenness of wet equatorial Amazonian rainforests. This seasonal cycle is concordant with independent observations of solar radiation. Weattribute alternative conclusions to an incomplete study of the seasonal cycle, i. e. the dry season only, and to prognostications based on a biased radiative transfer model. Consequently, evidence of dry season greening in geometry corrected satellite data was ignored and the absence of evidence for seasonal variation in lidar data due to noisy and saturated signals was misinterpreted as evidence of the absence of changes during the dry season. Our results, grounded in the physics of radiative transfer, buttress previous reports of dry season increases in leaf flushing, litterfall, photosynthesis and evapotranspiration in well-hydrated Amazonian rainforests.
  • Keywords: remote sensing, Amazonian rainforests, seasonality, MISR, MODIS
  • Keywords: remote sensing, Amazonian rainforests, seasonality, MISR, MODIS
  • Keywords: remote sensing, Amazonian rainforests, seasonality, MISR, MODIS
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